{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1OxwhJsXldLJpZQEdPYOUVcnVTJMhCUKwy7riQy5tFbHijlWFALwjw0yTIgli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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dbebf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('Median value of cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R9setac+jFWRVYlZF77oZyJyZT8xQW4FvAR3A7yVJcgDtwGpZlvslSfoFMUNu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\nD4+2sApPsfD4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dcadeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((365, 16), (366, 16))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test = data[:365],data[365:]\n",
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((365, 16), (356, 16))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除y大于8100的样本\n",
    "train = train[train.cnt < 8100]\n",
    "test = test[test.cnt < 8100]\n",
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((363, 16), (354, 16))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train[train.cnt > 600]\n",
    "test = test[test.cnt > 600]\n",
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入属性的直方图／分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pocCNwCvavI+kmfXxUTQDlT6QmW9qfzu4jmYwyEbghMz89oqfaD1geOattXQk8ILMPIqm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df37710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.instant.values, bins=30, kde=False)\n",
    "plt.xlabel('record number', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e129668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.season.values, bins=12, kde=False)\n",
    "plt.xlabel('season', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GIDPv5+L/CU1l5uo9y8vAztF3NThrx5eZ/wsQEdcA7wQ+PIg6hneHiLgC+Bzwycz81Lj7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\n3pJUIMNbkgr0/7Sw4/zGxDQ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e162b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.mnth.values, bins=24, kde=False)\n",
    "plt.xlabel('mnth', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JTTWwZP5sPu0ZwcTp2dps2tfMnTaJRbOn88sq6zZbxvAx40g1ZLXZuCnTGdz3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ZcvX8XD3Y38+fOZc/pw/OQpADzcShI2a3qadc2dNolKFSsAkJKSgp293RvnsRZ56hQN65uz/VMd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e129978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('cnt_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weathersit and temp = 0.99\n",
      "casual and registered = 0.95\n",
      "instant and season = 0.87\n",
      "dteday and yr = 0.83\n",
      "windspeed and registered = 0.67\n",
      "instant and casual = 0.66\n",
      "temp and registered = 0.63\n",
      "instant and registered = 0.63\n",
      "weathersit and registered = 0.63\n",
      "season and casual = 0.59\n",
      "workingday and atemp = 0.59\n",
      "season and registered = 0.57\n",
      "temp and casual = 0.54\n",
      "temp and windspeed = 0.54\n",
      "weathersit and windspeed = 0.54\n",
      "weathersit and casual = 0.54\n",
      "weekday and windspeed = 0.52\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
       "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
       "       'casual', 'registered', 'cnt'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((363, 9), (354, 9))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('temp',axis=1,inplace=True)\n",
    "train.drop('casual',axis=1,inplace=True)\n",
    "train.drop('registered',axis=1,inplace=True)\n",
    "train.drop('yr',axis=1,inplace=True)\n",
    "train.drop('dteday',axis=1,inplace=True)\n",
    "train.drop('instant',axis=1,inplace=True)\n",
    "train.drop('season',axis=1,inplace=True)\n",
    "\n",
    "test.drop('temp',axis=1,inplace=True)\n",
    "test.drop('casual',axis=1,inplace=True)\n",
    "test.drop('registered',axis=1,inplace=True)\n",
    "test.drop('yr',axis=1,inplace=True)\n",
    "test.drop('dteday',axis=1,inplace=True)\n",
    "test.drop('instant',axis=1,inplace=True)\n",
    "test.drop('season',axis=1,inplace=True)\n",
    "\n",
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=train.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = train.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qBs45Zgu//A/b9ybw+nPP4+XlRUjZcjzwt0Z4eXkRWrYcwQH+/Hn2jCfCvy4B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D6fj7bXXUW5MmWIivQw8L1pvPXf6ZSMi2VQr4cBqFKpIt0HDSW6RBTNGzXksgsvCHPtzsyVFzVn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc29dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('cnt_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atemp and cnt = 0.77\n",
      "weathersit and hum = 0.58\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['cnt'].values\n",
    "X_train = train.drop('cnt', axis = 1)\n",
    "y_test = test['cnt'].values\n",
    "X_test = test.drop('cnt', axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['mnth', 'holiday', 'weekday', 'workingday', 'weathersit', 'atemp',\n",
       "        'hum', 'windspeed'], dtype=object),\n",
       " array(['mnth', 'holiday', 'weekday', 'workingday', 'weathersit', 'atemp',\n",
       "        'hum', 'windspeed'], dtype=object),\n",
       " (363, 8),\n",
       " (354, 8))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns.values,X_test.columns.values,X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((363,), (354,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "# y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "# y_test = ss_y.transform(y_test.reshape(-1, 1))\n",
    "\n",
    "y_train = preprocessing.scale(y_train)\n",
    "y_test = preprocessing.scale(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.679938</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.224295</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.034416</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009011</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.037918</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.061150</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.128444</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.212228</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       coef     columns\n",
       "5  0.679938       atemp\n",
       "0  0.224295        mnth\n",
       "2  0.034416     weekday\n",
       "3  0.009011  workingday\n",
       "1 -0.037918     holiday\n",
       "6 -0.061150         hum\n",
       "7 -0.128444   windspeed\n",
       "4 -0.212228  weathersit"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.637887831158\n",
      "The r2 score of LinearRegression on train is 0.726535745518\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jaW9fu2YhXV2dgyh7dGnV3lu1bxjZ3js66tuuq2tcXdvNnDnY569vv/Wot8YS+O+9NTXa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4ae0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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46T1Ka00FkBocroC5E5vdyRmjejJ/lnv3ZEvhiYB9Q6VVVrYW+hcmdNit7Pry\nVX7Y+il6g5ER06/jlNHntDquuq6BL7ceZd32YzidbhunjOzJ5bOHUV1rY+1W3xMMy6qsjd3LbdUP\nBZpsoDqZY5tAdTDne/4eGDlzug6Bdpym5/ZhzsR+zfI3Br3/SmAvmwuKkZ7haaaEwHl4vQ4qanz3\nIZUfKyT/P4upKTtEatYp5J53J2k5AwJer7GSt9LKuh1HSbQYuXj6YE3b9m3VDwWabBBuiVFFxwjU\nKvBKoBOllNeF3pz4oGXtiT8CVen60nq5ZMYgdu4v5Uip7w7zepujMWFqtQfuqXa6IC3JSGVt62Z2\nW10VNWWHGTT+QsTUKzEY269N640stGzbt1U/FGiyQbgkRhWhIdASaY3n7/NxSyy8gbtNYD7NNV0U\nHpolIqusZKYGFodqb2/Le6v3+3UuwZCSZGp0MLUVx9AbTViSM+g+IJcZP3uOlIzeQV/bG1n4cqJT\nx/Tmgikn50q3VT8UaLKB6mSObQItkV4HEELcDEzxNCoihHgH2BAZ8zoXwUoqaultqbXaWbstNJ3P\njdesb8DlclG0axU7V71IRu9TmXTRg+h0ug45FzgZWfhyon17p7fSGWmr50p1MndOtGxTdwMyAa9M\nWg8gJWwWdVLCLam47PO9AZO2CUYd9ob2DSgqLj7B7tV/5cDutRhNifQZfmbQ9rWkZWTRlhNtK5pT\nncydEy0O5lFgmxBiHW75htNx18UomhDOWUdWu4PdPwQWEUxPMQfU3m1J8Q9b2PrZ09RXl5LZ51TG\nzr2jXUpzZpOexAQj5TU2LCb3Bz0UDYVtOSLVydy50DK25P+EECuAM3BvTy+UUh4Pu2WdDK2Njk07\npUFbE15FtZWyKlvA+7fHudjqqtj08R9xNNiY+9ObSOp/DjZn+7o/zhzdu1k04bVTRRaKpmgZW2LC\nrTw3HHfkcrsQ4nEpZeB3fBfC4XTy/pp91PiRn8wdlo3RoGPZigLyC4opqbRiMekBHVabo81JAQa9\nrrH4qCN4NVtMiamMOec2EtO6Y+o1BHs75DGzWtiqGgoVgdCyRHoOKAbG4W4VGAK8TAemOwohLgJ+\nKqVcEOw1Ygl/+ipNR7C2PKZpPsVfMtjhdLLs8wI27inukHNxuZzs3/QRRTtXMnXBExgTLPQadgbg\n3q42G/VYG9r2Mt7GQxWhKLSiRfR7vJTy14BdSlkLXAPkBntDIcQS4DGN9455AiV3k8zuYrMGh0tT\nH1F+wYltAdGrAAAQU0lEQVRGkXCH08lvX/2WVfmHqarzHRlpoa7qBBve+w27v3wNa10FNWWtd6L8\nOS+DXtdY9p83oS8/O2+4ci6KdqElgnF5lkne92E2HYvW1wMfEvxkgpgiUHK3vNraqPyvpe+otKqe\n4vI6+uaksGzFXoqKOyYceFiuZfuK57Fba+gxeBKjZ9+MOSm91XG2BidTR/Vkz4FySqvqSU82M3ZY\nNhdPH0R1rV3lVRRBo8XBPIV74H1PIcRTuKc8/ratkzxC4YtaPP0zKeXbQogZWg2M9cFrqd0SyclI\n5HhZa2V67zAvwO8xTXG54Jn3tzFxRE+27O1Y5/Setf9H4TfvYzCaOS3vZk45bbbfKYrdMxK544rx\nAJRVWslIM/sdQBYq4mm4WKiJJXsiMXjtE2ATMBP3NvUFUsptbZ0kpXwZd66mQ3SGwWujB2f5zMGM\nHJDB7sJicLkYOSCjTQcD7t2gpoPQgqX7oImUFO1kzJxftlk05x06Bu43RKABZKGgsw4XiwSxZE8o\nBq9pcTBfSSlPBXZpN61r0bLKND3FTFqqmfU7jzaqyllMevrmJFNTZ6esOvQbcE6Hnb0b3qHfqDyS\nuvUgs/dwzpj/WMDZz3od9MlJ4ZIZqjFeER60OJitnskC3wCNX8FSygNhs6qTYdDruXj6YM4a3Qt0\nOlblH2LV5uYjX+ttToqKa5g8sgf/3Xmsw1vOTakqOUj+J4upPL6fmopjjDvvToA2B8s7XXDweDXv\nrd4fsJVBoQgWLQ7mdM+fpnRID0ZKuRpYHez5sURLpbWMVBM19f7HbcsfyjqkidsUl8vFD1s/Ydea\n13A6bPQblcfIGde3+zpKU0URLrRU8io9mAC0rG8pbaPitrzaxhmjerJuh28hJq1Ya8rZ8tnTFH+/\nmQRLKrnn3Umvof4n+pqMbq1bXyhNFUW4CKQH0xt4FhgKrAXul1KWR8qwzkAwM4YyUs1cPnsYiRYj\n+QUnKK2q9yv2HQin00H5kQJy+o9lzJxfYknJDHj83ZeP5fkPdvkUCleaKopwEajY7VVgD3APYAEW\nR8SiTkR7dHW9jBM5GPQ68sb35aFrJ3D3/LGaz22w1VFZ/D0AialZTF3wBJN+8lCbzsWgg97ZKYwf\nnuPzdaWpoggXgZZIfaSUcwCEEF8AWyJjUuchUINjotmA0+nC6mn0sZgMTB7VA5fLxQMvbmhUxk80\nGzX1GZUdluR/shinw85ZVy/BZEnRrNnicMH7a/azIM89O0hpqigiRSAH05hMkFLahRCqubEFgZTY\nZo7vxwVT+lNcXgcuFzkZSby/Zl8rQaq25hM5nQ4K//sueze8g8vlYvDEizAmtH85s6XgBJfOHKI0\nVRQRpT3lmqHcWY0bTtbAuLuk9Tr39u+3u45iszU0dh0Hk6+pKTtC/ieLKT9aQGJqDmPn3k5Wv1FB\n2Vlec1LFX2mqKCJFIAczUgixv8njPp7HOsAlpVTVWZxUWnM4nKzKP4xHXJ/i8vpmHdLtzde4XC7y\nP32K8qMF9Bk+nVGzfk6CpbWQYEqiEZfTRY3VEfB6mSqRq4gCgRyMqrzSiNXuYNs+3zOGvDUmKUkJ\nmE2GRtV/f3g1W3Q6HaPzFlJVcpA+w8/yeeyN549grMjxaM3sZUvBCb/jZFUiVxENAol+/xBJQzoz\npZX1fgvnms6Vbsu5HP9uE9u/eIFJ8x4gNfsU0nIGkpbjvwxp63clnD7KPWTzqnMEl84cQmllPZ99\ne4DthaWU11jJ9KHir1BEivC2zHYRVmzyPwxNy1xp9xTF1/lh63/Q6Y1UHN9PanbbDqHgQHnj+FVw\nJ517ZSVz7dxTm0lz+lLxVygigXIwHcRqd7Ct8ITf10cPzgw4V7ri2D7yP1lMdWmRZ4riooBRS1NK\nq6yUVtbTKyu51WsqkauIBeJCVS6atJW8zZvgHgGbkdp6OuLRwg2sfeteqkuLGJh7PtMW/Emzc/Hy\n8frvG1XwFIpYQ0UwHSRQsV1WmoXMNAvmBAPJiaZWfUqZfUaQljOA4VOvJGdAcCqkG3YeY+/B8oCi\n4QpFtFDvxg7iLbbzRe6wbACKjldRXWd3T1HcvYZj+78FwJSYxrQFT/p0LllpFs4a05P0lLbnQntF\nw99eWdiB30ShCD0qggkBvsaaThndi9o6Gw+8uIGSSiu2+mp2fPECh+VXJHXrQc6AcY3b0U2ZOa4P\ncyb2a6yyXbaiwGelsC+U7IIi1lAOJgT4Gmv6yTcHWbnJLTp14sA2tny6hPrqEjJ6D2fs3DvQ6086\nAZ3OXQjn7QtquszxOq+12460uc2tZBcUsYZyMCHEu3NjtTvYsOMIToedPWvfYP+mf6LTGxBTr2Dw\nxJ80cy4Aiy4dw9C+6T4jD6/zmnfmIN76vIDdP5T61ZxRsguKWCOiDkYI0Q14A0gDTMCdUsqvI2lD\nJKiotrqbHNFRemg3yRm9yT13Eek9h/o8Pn/vCUYNzAp4zSSzkevPH4HV7uCNz6RPwSpVrauINSId\nwdwJfCGlfEoIIYC3cE+MjBucTif79+4gJ909pmT8Bb8iwZKMMcHi95xthSVYZzo0OQdzgoFrzxve\nKFilZBcUsUykHcxiTuoTGIE2J7bH+lykphw6dIhrr72W1atXc+8f/sZxkkhMDRyZgDt3YjAlkJPd\numDOH7dfPp56W4PmOUbR/rdpSSzZE0u2QGzZE4m5SEERYPDat0KInriXSne0dZ3OMBcJ4KOPPuDu\nu2+nvLyc2bPncMsVM3h3zeFmo0xqrQ0+E7UZqRYcNntQ9muZYxTtf5uWxJI9sWQLxJY9kZqLFBT+\nBq8JIU4D/g7cLaVcE677R4qqqkruv/8e3nnnLRITE3niicVcc811dO+exoK8lGY7Sy0Fp7yo3Iki\nXol0kncE8C4wX0q5NZL3Dhd//vMTvPPOW4wZk8vzz7/EkCHNE7lNe4J81cuo3Ikinol0DuYx3ALi\nS9w5XiqklD+OsA0dpqGhAYPBXSR31133kpWVzcKFt5CQkBDwPF/1MipyUcQzEXUwndGZtKSwcC83\n33wDN9ywkEsvvZzU1DRuu63NVFIzVKezoqugepE04nK5eO21lzn77Gls2ZJPfv6maJukUMQ8qpJX\nA8ePH2fRolv4/PPPSE9P59lnX+CCC+ZF2yyFIuZRDqYNvvtuPz/6UR4nTpzgrLNm8swzz9Orl7Z5\nRApFV0c5mDbo338AubnjmT59JjfcsBC90ltRKDSjHIwPNm/eyDffbGDhwlvR6/W88cY7rWQVFApF\n2ygH04SGhgaWLPkzTz75OABz5/6IAQMGKueiUASJcjAevvtuP7fc8gs2bvyGXr168+yzLzBgQPv0\ncRUKRXNUQgH4+9/fZNasaWzc+A3z5v2ENWu+5swzp0fbLIWi06MiGGDHjm3o9XqWLn2Riy++VC2J\nFIoQ0WUjmM2bN+JyuQdJ/8//PMzq1eu55JL5yrkoFCGkyzmYuro6/ud/7mXu3Fm8/PILACQmJtKv\nnxqtqlCEmi61RNq+fRs333wDUu5h6NBhTJo0OdomKRRxTZeIYJxOJ88+u4S5c2ci5R6uv/4XfP75\nl4wePTbapikUcU2XiGCWL/+URx55kO7de7BkyXOcffY50TZJoegSxLWDaWhowGg0MmfOufzmN79n\n/vwFZGdnR9sshaLLEJdLpIqKchYuvJ5f/epOAHQ6Hbfc8kvlXBSKCBN3Dmb9+rXMmHEG//jHu+za\ntZO6urpom6RQdFkircmbDCwDMgAbcI2U8lAorm2z2fjd737Ds88+hV6v55577mfRonswGuN6FahQ\nxDSRjmB+DmySUp6Fe2zJvaG4qN1uZ+rUqTzzzGIGDBjIv/61nHvuuV85F4UiykRak/cpIYRX5foU\noLytc7QOXjvvvPPIzc3lL3/5CykpKR20NDTE0wCtUBNL9sSSLRBb9nTUFp23XD7UtDF4bSVwGjBb\nSrkl0HWKi6s0GZidncKJE9XBGRsGOusArUgQS/bEki0QW/a0c/Cazx6biA9e87w2SwgxHPg3MDgU\n91M9RApF7BHRHIwQ4n4hxFWeh9VA6zmqCoUiboh0FvQV4HXP8skA/CzC91coFBEk0kneY8DcSN5T\noVBEj7grtFMoFLGDcjAKhSJshG2bWqFQKFQEo1AowoZyMAqFImwoB6NQKMKGcjAKhSJsKAejUCjC\nhnIwCoUibCgHo1AowkbcKDKFUy0vSHu64RbVSgNMwJ1Syq+jZY/HpouAn0opF0Th3npgKTAGsAI3\nSCkLI21HC5tOB/4opZwRZTsScPfpDQDMwO+llB9F0R4D8CIgABewUEq5I5hrxVMEExa1vA5wJ/CF\nlHI6cC3wXDSNEUIsAR4jev/n8wCLlHIKcB/w5yjZAYAQ4l7gJcASTTs8XAmUSCnPxN2r92yU7bkA\nQEo5FXgAeDTYC8WNg5FSPsXJfwhNanlhZjHwgudnI1AfRVsA1gM3RfH+04BPAaSUG4AJUbQFYB/w\nkyjb4OVd4EHPzzqgIYq2IKX8EPiF52F/OvBZ6pRLJK1qeTFiT0/cEdUdUbblbSHEjEjY4Ic0oKLJ\nY4cQwiiljMqHSUr5vhBiQDTu3RIpZTWAECIVeA931BBVpJQNQojXgYuAS4K9Tqd0MJFWywvWHiHE\nacDfgbullGuiaUsMUAk0FXjVR8u5xCJCiH7AB8BSKeWyaNsDIKW8RgjxK+C/QogRUsqa9l4jbpZI\nsaaWJ4QYgTv0XSCl/CSatsQI64DzAIQQk4Ht0TUndhBC9ACWA7+SUr4SA/ZcJYS43/OwFnB6/rSb\nThnB+CHW1PIew51AXCKEAKiQUv44uiZFlQ+A2UKI9bjzDNH+/4klfo179/NBIYQ3F3OulDJaUwP/\nAbwqhPgSSADuCNYWJdegUCjCRtwskRQKReyhHIxCoQgbysEoFIqwoRyMQqEIG8rBKBSKsBFP29SK\nECCEeA6YirtBcwiwy/PSEinlq1Gw5xFgYzSb/xTBo7apFT7xlNGvllIOiLIpik6MimAUmhFCPAxM\nxt1M+ixwKfCwlHJ1U4fkqUx9AeiHuwL0finlCh/XGoa7nSMLeEFK+SchxLXANUA28DHQ23Pd14QQ\ni4CFuKu0P5ZS/krLvRTRQ+VgFO3FIqUcIaVcGuCYJcArUsrxwIXAC55GvpaMAs4GxgM3CiHGeZ7v\nC+RKKX/tPVAIMQm4GZgEjAbGCyHGt+NeiiigIhhFe/mvhmPygOGe/Am4y80HA1taHPdWk07ij4BZ\nwAlgs49GyLNwRy3ejuw8z3la76WIAsrBKNpL054UF+6+InB/sL0YgFlSylIAIURv4JiPazV1Ivom\nj331vdibPvBcs7Yd91JEAbVEUnSEE8BIz8/zmjy/EvdyxttVvg1I8nH+RUIIkxAiA7eK2vIA9/oK\nOFcIkSKEMAJv4Rat0novRRRQDkbREZ4AbhZCbAYSmzx/GzBZCLENeBu4SkpZ5eP8OmAt8DXwmJRy\nl49jAJBSbsadWP4a2Ap86Unmar2XIgqobWpFVPDsIiGlfDi6lijCiYpgFApF2FARjEKhCBsqglEo\nFGFDORiFQhE2lINRKBRhQzkYhUIRNpSDUSgUYeP/ASd/6OIM8PtOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e447198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.637283731018\n",
      "The r2 score of RidgeCV on train is 0.726308675146\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "n_alphas = 20\n",
    "alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112043cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 7.84759970351\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.679938</td>\n",
       "      <td>0.665557</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.224295</td>\n",
       "      <td>0.223082</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.034416</td>\n",
       "      <td>0.033252</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.037918</td>\n",
       "      <td>-0.037340</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.061150</td>\n",
       "      <td>-0.057732</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.128444</td>\n",
       "      <td>-0.127242</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.212228</td>\n",
       "      <td>-0.211018</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lr  coef_ridge     columns\n",
       "5  0.679938    0.665557       atemp\n",
       "0  0.224295    0.223082        mnth\n",
       "2  0.034416    0.033252     weekday\n",
       "3  0.009011    0.009477  workingday\n",
       "1 -0.037918   -0.037340     holiday\n",
       "6 -0.061150   -0.057732         hum\n",
       "7 -0.128444   -0.127242   windspeed\n",
       "4 -0.212228   -0.211018  weathersit"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.597367573301\n",
      "The r2 score of LassoCV on train is 0.692647378163\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)  \n",
    "#lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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27XAkwqbKGgryslk237s5f0UkMVQA0tCY4jw+fd1CusJR7lpXRWtb14hst3L/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\nIiIZSgVARCRD/X9F5yV8/aSkMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11204fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.1\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.608321</td>\n",
       "      <td>0.679938</td>\n",
       "      <td>0.665557</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.148089</td>\n",
       "      <td>0.224295</td>\n",
       "      <td>0.223082</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034416</td>\n",
       "      <td>0.033252</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.037918</td>\n",
       "      <td>-0.037340</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.061150</td>\n",
       "      <td>-0.057732</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.046327</td>\n",
       "      <td>-0.128444</td>\n",
       "      <td>-0.127242</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.157863</td>\n",
       "      <td>-0.212228</td>\n",
       "      <td>-0.211018</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coef_lasso   coef_lr  coef_ridge     columns\n",
       "5    0.608321  0.679938    0.665557       atemp\n",
       "0    0.148089  0.224295    0.223082        mnth\n",
       "2    0.000000  0.034416    0.033252     weekday\n",
       "3    0.000000  0.009011    0.009477  workingday\n",
       "1   -0.000000 -0.037918   -0.037340     holiday\n",
       "6   -0.000000 -0.061150   -0.057732         hum\n",
       "7   -0.046327 -0.128444   -0.127242   windspeed\n",
       "4   -0.157863 -0.212228   -0.211018  weathersit"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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